NDT-Map-Code: A 3D global descriptor for real-time loop closure detection in lidar SLAM

TL;DR

This paper introduces NDT-Map-Code, a novel global descriptor for lidar SLAM that efficiently detects loop closures using NDT maps, suitable for diverse environments with improved performance over existing methods.

Contribution

The paper presents NDT-Map-Code, a scalable and low-maintenance global descriptor derived from NDT maps, enabling real-time loop closure detection without dense point clouds.

Findings

Outperforms state-of-the-art methods on KITTI and underground parking datasets.

Efficiently encodes spatial patterns from NDT maps for robust place recognition.

Applicable to both on-road and underground environments.

Abstract

Loop-closure detection, also known as place recognition, aiming to identify previously visited locations, is an essential component of a SLAM system. Existing research on lidar-based loop closure heavily relies on dense point cloud and 360 FOV lidars. This paper proposes an out-of-the-box NDT (Normal Distribution Transform) based global descriptor, NDT-Map-Code, designed for both on-road driving and underground valet parking scenarios. NDT-Map-Code can be directly extracted from the NDT map without the need for a dense point cloud, resulting in excellent scalability and low maintenance cost. The NDT representation is leveraged to identify representative patterns, which are further encoded according to their spatial location (bearing, range, and height). Experimental results on the NIO underground parking lot dataset and the KITTI dataset demonstrate that our method achieves significantly better performance compared to the state-of-the-art.